{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/implementing-lightweight-intrusion-detection","title":"Implementing Lightweight Intrusion Detection System on Resource Constrained Devices","arxiv_id":null,"date":"2024-10-28","proceeding":"2024 Cyber Awareness and Research Symposium (CARS) 2024 10","authors":["Charles Stolz","Fuhao Li","Jielun Zhang"],"abstract":"The rapid growth of Internet of Things (IoT) devices has increased the risk of network intrusions, which need effective security solutions for devices with low computational capability. However, Deep Learning based Intrusion Detection Systems (IDS) often demand substantial computational resources, which are unsuitable for the resource constrained IoT devices. To tackle this issue, we propose a lightweight IDS for Raspberry Pi operation. The proposed architecture includes traffic capture, threat detection, and alerting modules, utilizing signature and anomaly-based techniques. The signature-based module detects known attacks using predefined patterns, while the machine learning-based anomaly detection module identifies new threats by monitoring deviations from normal network behavior. The evaluation results show that our proposed scheme can detect a wide range of threats with minimal computational overhead.","url_abs":"https://ieeexplore.ieee.org/document/10778716","url_pdf":"https://ieeexplore.ieee.org/document/10778716","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"implementing-lightweight-intrusion-detection","repo_url":"https://github.com/rylandtikes/Lightweight-IDS","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"},{"task_slug":"intrusion-detection","task_name":"Intrusion Detection"},{"task_slug":"network-intrusion-detection","task_name":"Network Intrusion Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/intrusion-detection-on-cicids2017","task":"Intrusion Detection","dataset":"CICIDS2017","model":"K-Nearest Neighbors","rank_in_archive_order":1,"of":2,"metrics":{"Accuracy (%)":"98.07","F1 Score (Macro Avg)":"98.07","Precision (Macro Avg)":"98.08","Recall (Macro Avg)":"98.07"},"uses_additional_data":false},{"leaderboard":"/sota/intrusion-detection-on-cicids2017","task":"Intrusion Detection","dataset":"CICIDS2017","model":"Random Forest","rank_in_archive_order":2,"of":2,"metrics":{"Accuracy (%)":"98.13","F1 Score (Macro Avg)":"98.13","Precision (Macro Avg)":"98.14","Recall (Macro Avg)":"98.13"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}